Trickle: Code Propagation and Maintenance

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Trickle Code Propagation and Maintenance
Neil Patel UC Berkeley
David Culler UC Berkeley
Scott Shenker UC Berkeley ICSI
Philip Levis UC Berkeley
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Retasking a Sensor Net

• Long lifetimes require retasking
• Spectrum of retasking mechanisms
• Binary images (MOAP, XnP, Deluge)
• High-level virtual programs (Maté, TinyDB)
• Parameter setting (Attribute, Global)
• Install on every node
• Packet loss and transient disconnection
• Periodically check that everyone has the right
  code (advertise)
• TinyDB tuples embed query ID

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Propagating Can Be Costly,

• Binaries 10-60KB
• Virtual programs 20-400B
• Parameters 8-30B
• To every node in a large, multihop network

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Knowing When Is Costlier

• Periodically checking that everyone has the right
  code (advertisements) can cost more than the code
  itself.
• 64KB of data 1 packet/minute for a day
• 400B of data 1 packet/hour for a day
• 20B one packet!

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Problem Statement

• The first step is to detect when nodes need
  updates (continuous process)
• When there is no new code
• Maintenance cost should approach zero
• When there is new code
• Propagation should be rapid

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Relation to Deluge, SPIN, etc.

• When do you advertise code?
• How do you suppress control messages?
• Not a dissemination protocol

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Solution Trickle

• Simple, polite gossip algorithm
• Every once in a while, broadcast what code you
  have, unless youve heard some other nodes
  broadcast the same thing.
• Behavior (simulation and deployment)
• Scalability thousand-fold density changes
• Maintenance a few sends per hour
• Propagation less than a minute

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Outline

• Introduction
• Trickle algorithm
• Maintenance
• Propagation
• Conclusion

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Trickle Assumptions

• Wireless broadcast medium
• Concise, comparable metadata
• Given A and B, know which has newer code

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Idea Communication

• As long as each mote communicates with one other,
  needed updates will be detected
• In a single cell, one transmission will detect
  all needed updates
• Communication is reception or transmission
• Maintain a communication rate

(receptions transmissions) lt k
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Trickle Algorithm

• Time interval of length t
• Redundancy constant k (e.g., 1, 2)
• Pick a time t from 0, t
• Maintain a counter c, initialized to zero
• At time t, broadcast code metadata if c lt k
• Increment c when you hear identical metadata to
  your own
• At end of t, pick a new t

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Example Trickle Execution
k1
c
0
1
2
0
3
0
t
time
transmission
suppressed transmission
reception
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Example Trickle Execution
k1
c
0
1
t1a
2
0
3
0
t
time
transmission
suppressed transmission
reception
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Example Trickle Execution
k1
c
0
1
t1a
2
1
3
0
t
time
transmission
suppressed transmission
reception
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Example Trickle Execution
k1
c
0
1
t1a
2
1
3
0
t3a
t
time
transmission
suppressed transmission
reception
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Example Trickle Execution
k1
c
0
1
t1a
2
2
3
0
t3a
t
time
transmission
suppressed transmission
reception
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Example Trickle Execution
k1
c
0
1
t1a
2
2
t2a
3
0
t3a
t
time
transmission
suppressed transmission
reception
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Example Trickle Execution
k1
c
0
1
t1a
2
0
t2a
3
0
t3a
t
time
transmission
suppressed transmission
reception
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Example Trickle Execution
k1
c
1
1
t1a
2
0
t2a
t2b
3
1
t3a
t
time
transmission
suppressed transmission
reception
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Example Trickle Execution
k1
c
1
1
t1a
2
0
t2a
t2b
3
1
t3a
t3b
t
time
transmission
suppressed transmission
reception
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Example Trickle Execution
k1
c
1
1
t1a
t1b
2
0
t2a
t2b
3
1
t3a
t3b
t
time
transmission
suppressed transmission
reception
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Outline

• Problem statement
• Trickle algorithm
• Maintenance
• Propagation
• Conclusion

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Maintenance

• Minimize maintenance cost (transmissions) when
  there is no new code
• Keep as close to k as possible
• Start with three assumptions, relax each
• Lossless network
• Perfect synchronization
• Single-hop

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Ideal Case

• k transmissions per interval
• First k nodes to transmit suppress all others
• Independent of density

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Loss
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Logarithmic Behavior of Loss

• Transmission increase is due to the probability
  that one node has not heard n transmissions
• Example 10 loss
• 1 in 10 nodes will not hear one transmission
• 1 in 100 nodes will not hear two transmissions
• 1 in 1000 nodes will not hear three, etc.
• Fundamental bound to maintaining per-interval
  communication

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Synchronization
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Short Listen Effect

• Some nodes dont listen much (pick small t
  values)
• For example, B transmits three times

t
A
B
C
D
Time
transmission
suppressed transmission
reception
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Solution

• Add a listening period pick t from 0.5t, t

Listen-only period
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Effect of Listen Period
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Multi-Cell Case

• TOSSIM simulation
• No synchronization, loss from empirical model
• Nodes uniformly distributed in 50x50 area
• Logarithmic scaling holds

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Empirical Validation
(transmissions receptions)

• Redundancy
• Maté VM implementation

- k
intervals
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Outline

• Problem statement
• Trickle algorithm
• Maintenance
• Propagation
• Conclusion

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Choosing Intervals

• Large interval low cost, slow to discover
• Small interval high cost, quick to discover
• When theres new gossip, talk more
• When theres nothing new, talk less

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Speeding Propagation

• Adjust t tl, th
• When t expires, double t up to th
• When you hear newer metadata, set t to tl
• When you hear newer code, set t to tl
• When you hear older metadata, send an update

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Rate Change Illustration
Hear Newer Metadata
2tl
th
tl
th
th
2
Time
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Simulated Propagation
k1, tl1 second, th1 minute
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Empirical Propagation

• Deployed 19 nodes in office setting
• Instrumented nodes for accurate time measurements
• Introduce new code, log installation times
• k1, tl1 second, th1 minute
• 40 test runs

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Network Layout
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Empirical Results
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Changing th to 20 minutes
k1
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Conclusions

• Trickle efficiency scales logarithmically with
  density
• Can obtain rapid propagation with low maintenance
• At most 3 sends/hour, propagates in 30 seconds
• Uses beyond code propagation
• Changes to data such as routing tables
• E.g., predicates can scope distance
• Further examination of tl, th and k needed

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Questions

• Tech report available in back

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